Plotting ROC Curve with Multiple Classes

Question

I am following the documentation for plotting ROC curves for multiple classes at this link: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

I am confused about this line in particular:

y_score = classifier.fit(X_train, y_train).decision_function(X_test)

I've seen that in other examples, y_score holds probabilities, and they are all positive values, as we would expect. However, the y_score (each column for classes A-C) in this example has mostly negative values. Interestingly, they still add up to -1:

In: y_score[0:5,:]
Out: array([[-0.76305896, -0.36472635,  0.1239796 ],
            [-0.20238399, -0.63148982, -0.16616656],
            [ 0.11808492, -0.80262259, -0.32062486],
            [-0.90750303, -0.1239792 ,  0.02184016],
            [-0.01108555, -0.27918155, -0.71882525]])

How am I supposed to interpret this? And how can I tell just from the y_score which class is the model's prediction for each input?

Edit: all the relevant code:

import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle

from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp

# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]

# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
                                                    random_state=0)

# Learn to predict each class against the other
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', 
                                 probability=True,
                                 random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)

It's all available in the link above, but I will include it as an edit in the original post — bcarmel
Possible duplicate of stackoverflow.com/questions/46820154/… — Sumit S Chawla

seralouk seralouk · Accepted Answer · 2018-07-19T13:51:33

To actually plot the multi-class ROC use label_binarize function.

Example using Iris data:

import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.metrics import roc_curve, auc
from sklearn.multiclass import OneVsRestClassifier

iris = datasets.load_iris()
X = iris.data
y = iris.target

# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)

classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                 random_state=0))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)

fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
colors = cycle(['blue', 'red', 'green'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([-0.05, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic for multi-class data')
plt.legend(loc="lower right")
plt.show()

Plotting ROC Curve with Multiple Classes

2 Answers